Transporting Comparative Effectiveness Evidence Between Countries: Considerations for Health Technology Assessments.

Internal validity is often the primary concern for health technology assessment agencies when assessing comparative effectiveness evidence. However, the increasing use of real-world data from countries other than a health technology assessment agency's target population in effectiveness research has increased concerns over the external validity, or "transportability", of this evidence, and has led to a preference for local data. Methods have been developed to enable a lack of transportability to be addressed, for example by accounting for cross-country differences in disease characteristics, but their consideration in health technology assessments is limited. This may be because of limited knowledge of the methods and/or uncertainties in how best to utilise them within existing health technology assessment frameworks. This article aims to provide an introduction to transportability, including a summary of its assumptions and the methods available for identifying and adjusting for a lack of transportability, before discussing important considerations relating to their use in health technology assessment settings, including guidance on the identification of effect modifiers, guidance on the choice of target population, estimand, study sample and methods, and how evaluations of transportability can be integrated into health technology assessment submission and decision processes.

Modeling the multi-state natural history of rare diseases with heterogeneous individual patient data: A simulation study.

Multi-state survival models are used to represent the natural history of a disease, forming the basis of a health technology assessment comparing a novel treatment to current practice. Constructing such models for rare diseases is problematic, since evidence sources are typically much sparser and more heterogeneous. This simulation study investigated different one-stage and two-stage approaches to meta-analyzing individual patient data (IPD) in a multi-state survival setting when the number and size of studies being meta-analyzed are small. The objective was to assess methods of different complexity to see when they are accurate, when they are inaccurate and when they struggle to converge due to the sparsity of data. Biologically plausible multi-state IPD were simulated from study- and transition-specific hazard functions. One-stage frailty and two-stage stratified models were estimated, and compared to a base case model that did not account for study heterogeneity. Convergence and the bias/coverage of population-level transition probabilities to, and lengths of stay in, each state were used to assess model performance. A real-world application to Duchenne Muscular Dystrophy, a neuromuscular rare disease, was conducted, and a software demonstration is provided. Models not accounting for study heterogeneity were consistently out-performed by two-stage models. Frailty models struggled to converge, particularly in scenarios of low heterogeneity, and predictions from models that did converge were also subject to bias. Stratified models may be better suited to meta-analyzing disparate sources of IPD in rare disease natural history/economic modeling, as they converge more consistently and produce less biased predictions of lengths of stay.

Cost-Effectiveness Analysis of Siltuximab for Australian Public Investment in the Rare Condition Idiopathic Multicentric Castleman Disease.

OBJECTIVES: This paper presents an Australian model that formed part of the health technology assessment for public investment in siltuximab for the rare condition of idiopathic Multicentric Castleman Disease (iMCD) in Australia. METHODS: Two literature reviews were conducted to identify the appropriate comparator and model structure. Survival gain based on available clinical trial data were modelled using an Excel-based model semi-Markov model including time-varying transition probabilities, an adjustment for trial crossover and long-term data. A 20-year horizon was taken, and an Australian healthcare system perspective was adopted, with both benefits and costs discounted at 5%. The model was informed with an inclusive stakeholder approach that included a review of the model by an independent economist, Australian clinical expert opinion and feedback from the Pharmaceutical Benefits Advisory Committee (PBAC). The price used in the economic evaluation reflects a confidential discounted price, which was agreed to with the PBAC. RESULTS: An incremental cost-effectiveness ratio of A$84,935 per quality-adjusted life-year (QALY) gained was estimated. At a willingness-to-pay threshold of A$100,000 per QALY, siltuximab has a 72.1% probability of being cost-effective compared with placebo and best supportive care. Sensitivity analyses results were most sensitive to the length of interval between administrations (from 3- to 6-weekly) and crossover adjustments. CONCLUSION: Within a collaborative and inclusive stakeholder framework, the model submitted to the Australian PBAC found siltuximab to be cost-effective for the treatment of iMCD.

Incorporating single-arm studies in meta-analysis of randomised controlled trials: a simulation study.

BACKGROUND: Use of real world data (RWD) from non-randomised studies (e.g. single-arm studies) is increasingly being explored to overcome issues associated with data from randomised controlled trials (RCTs). We aimed to compare methods for pairwise meta-analysis of RCTs and single-arm studies using aggregate data, via a simulation study and application to an illustrative example. METHODS: We considered contrast-based methods proposed by Begg & Pilote (1991) and arm-based methods by Zhang et al (2019). We performed a simulation study with scenarios varying (i) the proportion of RCTs and single-arm studies in the synthesis (ii) the magnitude of bias, and (iii) between-study heterogeneity. We also applied methods to data from a published health technology assessment (HTA), including three RCTs and 11 single-arm studies. RESULTS: Our simulation study showed that the hierarchical power and commensurate prior methods by Zhang et al provided a consistent reduction in uncertainty, whilst maintaining over-coverage and small error in scenarios where there was limited RCT data, bias and differences in between-study heterogeneity between the two sets of data. The contrast-based methods provided a reduction in uncertainty, but performed worse in terms of coverage and error, unless there was no marked difference in heterogeneity between the two sets of data. CONCLUSIONS: The hierarchical power and commensurate prior methods provide the most robust approach to synthesising aggregate data from RCTs and single-arm studies, balancing the need to account for bias and differences in between-study heterogeneity, whilst reducing uncertainty in estimates. This work was restricted to considering a pairwise meta-analysis using aggregate data.

Multivariate network meta-analysis incorporating class effects.

BACKGROUND: Network meta-analysis synthesises data from a number of clinical trials in order to assess the comparative efficacy of multiple healthcare interventions in similar patient populations. In situations where clinical trial data are heterogeneously reported i.e. data are missing for one or more outcomes of interest, synthesising such data can lead to disconnected networks of evidence, increased uncertainty, and potentially biased estimates which can have severe implications for decision-making. To overcome this issue, strength can be borrowed between outcomes of interest in multivariate network meta-analyses. Furthermore, in situations where there are relatively few trials informing each treatment comparison, there is a potential issue with the sparsity of data in the treatment networks, which can lead to substantial parameter uncertainty. A multivariate network meta-analysis approach can be further extended to borrow strength between interventions of the same class using hierarchical models. METHODS: We extend the trivariate network meta-analysis model to incorporate the exchangeability between treatment effects belonging to the same class of intervention to increase precision in treatment effect estimates. We further incorporate a missing data framework to estimate uncertainty in trials that did not report measures of variability in order to maximise the use of all available information for healthcare decision-making. The methods are applied to a motivating dataset in overactive bladder syndrome. The outcomes of interest were mean change from baseline in incontinence, voiding and urgency episodes. All models were fitted using Bayesian Markov Chain Monte Carlo (MCMC) methods in WinBUGS. RESULTS: All models (univariate, multivariate, and multivariate models incorporating class effects) produced similar point estimates for all treatment effects. Incorporating class effects in multivariate models often increased precision in treatment effect estimates. CONCLUSIONS: Multivariate network meta-analysis incorporating class effects allowed for the comparison of all interventions across all outcome measures to ameliorate the potential impact of outcome reporting bias, and further borrowed strength between interventions belonging to the same class of treatment to increase the precision in treatment effect estimates for healthcare policy and decision-making.

Mixed-effects models for health care longitudinal data with an informative visiting process: A Monte Carlo simulation study.

Electronic health records are being increasingly used in medical research to answer more relevant and detailed clinical questions; however, they pose new and significant methodological challenges. For instance, observation times are likely correlated with the underlying disease severity: Patients with worse conditions utilise health care more and may have worse biomarker values recorded. Traditional methods for analysing longitudinal data assume independence between observation times and disease severity; yet, with health care data, such assumptions unlikely hold. Through Monte Carlo simulation, we compare different analytical approaches proposed to account for an informative visiting process to assess whether they lead to unbiased results. Furthermore, we formalise a joint model for the observation process and the longitudinal outcome within an extended joint modelling framework. We illustrate our results using data from a pragmatic trial on enhanced care for individuals with chronic kidney disease, and we introduce user-friendly software that can be used to fit the joint model for the observation process and a longitudinal outcome.

Impact of model misspecification in shared frailty survival models.

Survival models incorporating random effects to account for unmeasured heterogeneity are being increasingly used in biostatistical and applied research. Specifically, unmeasured covariates whose lack of inclusion in the model would lead to biased, inefficient results are commonly modeled by including a subject-specific (or cluster-specific) frailty term that follows a given distribution (eg, gamma or lognormal). Despite that, in the context of parametric frailty models, little is known about the impact of misspecifying the baseline hazard or the frailty distribution or both. Therefore, our aim is to quantify the impact of such misspecification in a wide variety of clinically plausible scenarios via Monte Carlo simulation, using open-source software readily available to applied researchers. We generate clustered survival data assuming various baseline hazard functions, including mixture distributions with turning points, and assess the impact of sample size, variance of the frailty, baseline hazard function, and frailty distribution. Models compared include standard parametric distributions and more flexible spline-based approaches; we also included semiparametric Cox models. The resulting bias can be clinically relevant. In conclusion, we highlight the importance of fitting models that are flexible enough and the importance of assessing model fit. We illustrate our conclusions with two applications using data on diabetic retinopathy and bladder cancer. Our results show the importance of assessing model fit with respect to the baseline hazard function and the distribution of the frailty: misspecifying the former leads to biased relative and absolute risk estimates, whereas misspecifying the latter affects absolute risk estimates and measures of heterogeneity.

Bayesian Multiparameter Evidence Synthesis to Inform Decision Making: A Case Study in Metastatic Hormone-Refractory Prostate Cancer.

In health technology assessment, decisions are based on complex cost-effectiveness models that require numerous input parameters. When not all relevant estimates are available, the model may have to be simplified. Multiparameter evidence synthesis combines data from diverse sources of evidence, which results in obtaining estimates required in clinical decision making that otherwise may not be available. We demonstrate how bivariate meta-analysis can be used to predict an unreported estimate of a treatment effect enabling implementation of a multistate Markov model, which otherwise needs to be simplified. To illustrate this, we used an example of cost-effectiveness analysis for docetaxel in combination with prednisolone in metastatic hormone-refractory prostate cancer. Bivariate meta-analysis was used to model jointly available data on treatment effects on overall survival and progression-free survival (PFS) to predict the unreported effect on PFS in a study evaluating docetaxel with prednisolone. The predicted treatment effect on PFS enabled implementation of a 3-state Markov model comprising stable disease, progressive disease, and dead states, while lack of the estimate restricted the model to a 2-state model (with alive and dead states). The 2-state and 3-state models were compared by calculating the incremental cost-effectiveness ratio (which was much lower in the 3-state model: £22,148 per quality-adjusted life year gained compared to £30,026 obtained from the 2-state model) and the expected value of perfect information (which increased with the 3-state model). The 3-state model has the advantage of distinguishing surviving patients who progressed from those who did not progress. Hence, the use of advanced meta-analytic techniques allowed obtaining relevant parameter estimates to populate a model describing disease pathway in more detail while helping to prevent valuable clinical data from being discarded.

Methods for Population-Adjusted Indirect Comparisons in Health Technology Appraisal.

Standard methods for indirect comparisons and network meta-analysis are based on aggregate data, with the key assumption that there is no difference between the trials in the distribution of effect-modifying variables. Methods which relax this assumption are becoming increasingly common for submissions to reimbursement agencies, such as the National Institute for Health and Care Excellence (NICE). These methods use individual patient data from a subset of trials to form population-adjusted indirect comparisons between treatments, in a specific target population. Recently proposed population adjustment methods include the Matching-Adjusted Indirect Comparison (MAIC) and the Simulated Treatment Comparison (STC). Despite increasing popularity, MAIC and STC remain largely untested. Furthermore, there is a lack of clarity about exactly how and when they should be applied in practice, and even whether the results are relevant to the decision problem. There is therefore a real and present risk that the assumptions being made in one submission to a reimbursement agency are fundamentally different to-or even incompatible with-the assumptions being made in another for the same indication. We describe the assumptions required for population-adjusted indirect comparisons, and demonstrate how these may be used to generate comparisons in any given target population. We distinguish between anchored and unanchored comparisons according to whether a common comparator arm is used or not. Unanchored comparisons make much stronger assumptions, which are widely regarded as infeasible. We provide recommendations on how and when population adjustment methods should be used, and the supporting analyses that are required to provide statistically valid, clinically meaningful, transparent and consistent results for the purposes of health technology appraisal. Simulation studies are needed to examine the properties of population adjustment methods and their robustness to breakdown of assumptions.

Assessing methods for dealing with treatment switching in clinical trials: A follow-up simulation study.

When patients randomised to the control group of a randomised controlled trial are allowed to switch onto the experimental treatment, intention-to-treat analyses of the treatment effect are confounded because the separation of randomised groups is lost. Previous research has investigated statistical methods that aim to estimate the treatment effect that would have been observed had this treatment switching not occurred and has demonstrated their performance in a limited set of scenarios. Here, we investigate these methods in a new range of realistic scenarios, allowing conclusions to be made based upon a broader evidence base. We simulated randomised controlled trials incorporating prognosis-related treatment switching and investigated the impact of sample size, reduced switching proportions, disease severity, and alternative data-generating models on the performance of adjustment methods, assessed through a comparison of bias, mean squared error, and coverage, related to the estimation of true restricted mean survival in the absence of switching in the control group. Rank preserving structural failure time models, inverse probability of censoring weights, and two-stage methods consistently produced less bias than the intention-to-treat analysis. The switching proportion was confirmed to be a key determinant of bias: sample size and censoring proportion were relatively less important. It is critical to determine the size of the treatment effect in terms of an acceleration factor (rather than a hazard ratio) to provide information on the likely bias associated with rank-preserving structural failure time model adjustments. In general, inverse probability of censoring weight methods are more volatile than other adjustment methods.

Long-term healthcare use and costs in patients with stable coronary artery disease: a population-based cohort using linked health records (CALIBER).

AIMS: To examine long-term healthcare utilization and costs of patients with stable coronary artery disease (SCAD). METHODS AND RESULTS: Linked cohort study of 94 966 patients with SCAD in England, 1 January 2001 to 31 March 2010, identified from primary care, secondary care, disease, and death registries. Resource use and costs, and cost predictors by time and 5-year cardiovascular disease (CVD) risk profile were estimated using generalized linear models. Coronary heart disease hospitalizations were 20.5% in the first year and 66% in the year following a non-fatal (myocardial infarction, ischaemic or haemorrhagic stroke) event. Mean healthcare costs were £3133 per patient in the first year and £10 377 in the year following a non-fatal event. First-year predictors of cost included sex (mean cost £549 lower in females), SCAD diagnosis (non-ST-elevation myocardial infarction cost £656 more than stable angina), and co-morbidities (heart failure cost £657 more per patient). Compared with lower risk patients (5-year CVD risk 3.5%), those of higher risk (5-year CVD risk 44.2%) had higher 5-year costs (£23 393 vs. £9335) and lower lifetime costs (£43 020 vs. £116 888). CONCLUSION: Patients with SCAD incur substantial healthcare utilization and costs, which varies and may be predicted by 5-year CVD risk profile. Higher risk patients have higher initial but lower lifetime costs than lower risk patients as a result of shorter life expectancy. Improved cardiovascular survivorship among an ageing CVD population is likely to require stratified care in anticipation of the burgeoning demand.

Using electronic health records to predict costs and outcomes in stable coronary artery disease.

OBJECTIVES: To use electronic health records (EHR) to predict lifetime costs and health outcomes of patients with stable coronary artery disease (stable-CAD) stratified by their risk of future cardiovascular events, and to evaluate the cost-effectiveness of treatments targeted at these populations. METHODS: The analysis was based on 94 966 patients with stable-CAD in England between 2001 and 2010, identified in four prospectively collected, linked EHR sources. Markov modelling was used to estimate lifetime costs and quality-adjusted life years (QALYs) stratified by baseline cardiovascular risk. RESULTS: For the lowest risk tenth of patients with stable-CAD, predicted discounted remaining lifetime healthcare costs and QALYs were £62 210 (95% CI £33 724 to £90 043) and 12.0 (95% CI 11.5 to 12.5) years, respectively. For the highest risk tenth of the population, the equivalent costs and QALYs were £35 549 (95% CI £31 679 to £39 615) and 2.9 (95% CI 2.6 to 3.1) years, respectively. A new treatment with a hazard reduction of 20% for myocardial infarction, stroke and cardiovascular disease death and no side-effects would be cost-effective if priced below £72 per year for the lowest risk patients and £646 per year for the highest risk patients. CONCLUSIONS: Existing EHRs may be used to estimate lifetime healthcare costs and outcomes of patients with stable-CAD. The stable-CAD model developed in this study lends itself to informing decisions about commissioning, pricing and reimbursement. At current prices, to be cost-effective some established as well as future stable-CAD treatments may require stratification by patient risk.

Joint modelling of longitudinal and survival data: incorporating delayed entry and an assessment of model misspecification.

A now common goal in medical research is to investigate the inter-relationships between a repeatedly measured biomarker, measured with error, and the time to an event of interest. This form of question can be tackled with a joint longitudinal-survival model, with the most common approach combining a longitudinal mixed effects model with a proportional hazards survival model, where the models are linked through shared random effects. In this article, we look at incorporating delayed entry (left truncation), which has received relatively little attention. The extension to delayed entry requires a second set of numerical integration, beyond that required in a standard joint model. We therefore implement two sets of fully adaptive Gauss-Hermite quadrature with nested Gauss-Kronrod quadrature (to allow time-dependent association structures), conducted simultaneously, to evaluate the likelihood. We evaluate fully adaptive quadrature compared with previously proposed non-adaptive quadrature through a simulation study, showing substantial improvements, both in terms of minimising bias and reducing computation time. We further investigate, through simulation, the consequences of misspecifying the longitudinal trajectory and its impact on estimates of association. Our scenarios showed the current value association structure to be very robust, compared with the rate of change that we found to be highly sensitive showing that assuming a simpler trend when the truth is more complex can lead to substantial bias. With emphasis on flexible parametric approaches, we generalise previous models by proposing the use of polynomials or splines to capture the longitudinal trend and restricted cubic splines to model the baseline log hazard function. The methods are illustrated on a dataset of breast cancer patients, modelling mammographic density jointly with survival, where we show how to incorporate density measurements prior to the at-risk period, to make use of all the available information. User-friendly Stata software is provided.

Searching for indirect evidence and extending the network of studies for network meta-analysis: case study in venous thromboembolic events prevention following elective total knee replacement surgery.

OBJECTIVE: To evaluate the effect of study identification methods and network size on the relative effectiveness and cost-effectiveness of recommended pharmacological venous thromboembolic events (VTEs) prophylaxis for adult patients undergoing elective total knee replacement surgery in the United Kingdom. METHODS: A stepwise literature search specifically designed to identify indirect evidence was conducted to extend the original clinical review from the latest National Institute for Health and Care Excellence (NICE) VTE technology appraisal. Different network sizes or network orders, based on the successive searches, informed three network meta-analyses (NMAs), which were compared with a replicated base case. The resulting comparative estimates were inputted in an economic model to investigate the effect of network size on cost-effectiveness probabilities. RESULTS: Searches increased the number of indirect comparisons between VTE interventions, progressively widening the relevant network of studies for NMA. Precision around mean relative treatment effects was increased as the network was extended from the base case to first-order NMA, but further extensions had limited effect. Cost-effectiveness analysis results were largely insensitive to variation in clinical inputs from the different NMA orders. CONCLUSIONS: No standard methodology is currently recommended by NICE to identify the most relevant network of studies for NMA. Our study showed that optimizing the identification of studies for NMA can extend the evidence base for analysis and reduce the uncertainty in relative effectiveness estimates. Although in our example network extensions did not affect the acceptability of available treatments in VTE prevention based on cost-effectiveness results, it may in other applications.

Randomised controlled trial and health economic evaluation of the impact of diagnostic testing for influenza, respiratory syncytial virus and Streptococcus pneumoniae infection on the management of acute admissions in the elderly and high-risk 18- to 64-year-olds.

BACKGROUND: Western industrialised nations face a large increase in the number of older people. People over the age of 60 years account for almost half of the 16.8 million hospital admissions in England from 2009 to 2010. During 2009-10, respiratory infections accounted for approximately 1 in 30 hospital admissions and 1 in 20 of the 51.5 million bed-days. OBJECTIVE: To determine the diagnostic accuracy and clinical effectiveness and cost-effectiveness of rapid molecular and near-patient diagnostic tests for influenza, respiratory syncytial virus (RSV) and Streptococcus pneumoniae infections in comparison with traditional laboratory culture. METHODS: We carried out a randomised controlled trial (RCT) to evaluate impact on prescribing and clinical outcomes of point-of-care tests (POCTs) for influenza A and B and pneumococcal infection, reverse transcriptase-polymerase chain reaction (RT-PCR) tests for influenza A and B and RSV A and B, and conventional culture for these pathogens. We evaluated diagnostic accuracy of POCTs for influenza and pneumococcal infection, RT-PCR for influenza and sputum culture for S. pneumoniae using samples collected during the RCT. We did a systematic review and meta-analysis of POCTs for influenza A and B. We evaluated ease and speed of use of each test, process outcomes and cost-effectiveness. RESULTS: There was no evidence of association between diagnostic group and prescribing or clinical outcomes. Using PCR as 'gold standard', Quidel Influenza A + B POCT detected 24.4% [95% confidence interval (CI) 16.0% to 34.6%] of influenza infections (specificity 99.7%, 95% CI 99.2% to 99.9%); viral culture detected 21.6% (95% CI 13.5% to 31.6%; specificity 99.8%, 95% CI 99.4% to 100%). Using blood culture as 'gold standard', BinaxNOW pneumococcal POCT detected 57.1% (95% CI 18.4% to 90.1%) of pneumococcal infections (specificity 92.5%; 95% CI 90.6% to 94.1%); sputum culture detected 100% (95% CI 2.5% to 100%; specificity 97.2%, 95% CI 94.3% to 98.9%). Overall, pooled estimates of sensitivity and specificity of POCTs for influenza from the literature were 74% (95% CI 67% to 80%) and 99% (95% CI 98% to 99%), respectively. Median intervals from specimen collection to test result were 15 minutes [interquartile range (IQR) 10-23 minutes) for Quidel Influenza A + B POCT, 20 minutes (IQR 15-30 minutes) for BinaxNOW pneumococcal POCT, 50.8 hours (IQR 44.3-92.6 hours) for semi-nested conventional PCR, 29.2 hours (IQR 26-46.9 hours) for real-time PCR, 629.6 hours (IQR 262.5-846.7 hours) for culture of influenza and 84.4 hours (IQR 70.7-137.8 hours) and 71.4 hours (IQR 69.15-84.0 hours) for culture of S. pneumoniae in blood and sputum, respectively. Both POCTs were rated straightforward and undemanding; blood culture was moderately complex and all other tests were complex. Costs and quality-adjusted life-years (QALYs) of each diagnostic strategy were similar. Incrementally, PCR was most cost-effective (78.3% probability at a willingness to pay of £20,000/QALY). Few patients were admitted within a timescale conducive to treatment with a neuraminidase inhibitor according to National Institute for Health and Care Excellence guidance. LIMITATIONS: The accuracy study was limited by inadequate gold standards. CONCLUSIONS: All tests had limitations. We found no evidence that POCTs for influenza or S. pneumoniae, or PCR for influenza or RSV influenced antimicrobial prescribing or clinical outcomes. The total costs and QALYs of each diagnostic strategy were similar, although, incrementally, PCR was the most cost-effective strategy. The analysis does not support routine use of POCTs for either influenza or pneumococcal antigen for adults presenting with acute cardiopulmonary conditions, but suggests that conventional viral culture for clinical diagnosis should be replaced by PCR. TRIAL REGISTRATION: Current Controlled Trials ISRCTN21521552. FUNDING: This project was funded by the NIHR Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 18, No. 36. See the NIHR Journals Library website for further project information.

Use of Bayesian multivariate meta-analysis to estimate the HAQ for mapping onto the EQ-5D questionnaire in rheumatoid arthritis.

BACKGROUND: In health technology assessment, decisions about reimbursement for new health technologies are largely based on effectiveness estimates. Sometimes, however, the target effectiveness estimates are not readily available. This may be because many alternative instruments measuring these outcomes are being used (and not all always reported) or an extended follow-up time of clinical trials is needed to evaluate long-term end points, leading to the limited data on the target clinical outcome. In the areas of highest priority in health care, decisions are required to be made on a short time scale. Therefore, alternative clinical outcomes, including surrogate end points, are increasingly being considered for use in evidence synthesis as part of economic evaluation. OBJECTIVE: To illustrate the potential effect of reduced uncertainty around the clinical outcome on the utility when estimating it from a multivariate meta-analysis. METHODS: Bayesian multivariate meta-analysis has been used to synthesize data on correlated outcomes in rheumatoid arthritis and to incorporate external data in the model in the form of informative prior distributions. Estimates of Health Assessment Questionnaire were then mapped onto the health-related quality-of-life measure EuroQol five-dimensional questionnaire, and the effect was compared with mapping the Health Assessment Questionnaire obtained from the univariate approach. RESULTS: The use of multivariate meta-analysis can lead to reduced uncertainty around the effectiveness parameter and ultimately uncertainty around the utility. CONCLUSIONS: By allowing all the relevant data to be incorporated in estimating clinical effectiveness outcomes, multivariate meta-analysis can improve the estimation of health utilities estimated through mapping methods. While reduced uncertainty may have an effect on decisions based on economic evaluation of new health technologies, the use of short-term surrogate end points can allow for early decisions. More research is needed to determine the circumstances under which uncertainty is reduced.

Adjusting survival time estimates to account for treatment switching in randomized controlled trials--an economic evaluation context: methods, limitations, and recommendations.

BACKGROUND: Treatment switching commonly occurs in clinical trials of novel interventions in the advanced or metastatic cancer setting. However, methods to adjust for switching have been used inconsistently and potentially inappropriately in health technology assessments (HTAs). OBJECTIVE: We present recommendations on the use of methods to adjust survival estimates in the presence of treatment switching in the context of economic evaluations. METHODS: We provide background on the treatment switching issue and summarize methods used to adjust for it in HTAs. We discuss the assumptions and limitations associated with adjustment methods and draw on results of a simulation study to make recommendations on their use. RESULTS: We demonstrate that methods used to adjust for treatment switching have important limitations and often produce bias in realistic scenarios. We present an analysis framework that aims to increase the probability that suitable adjustment methods can be identified on a case-by-case basis. We recommend that the characteristics of clinical trials, and the treatment switching mechanism observed within them, should be considered alongside the key assumptions of the adjustment methods. Key assumptions include the "no unmeasured confounders" assumption associated with the inverse probability of censoring weights (IPCW) method and the "common treatment effect" assumption associated with the rank preserving structural failure time model (RPSFTM). CONCLUSIONS: The limitations associated with switching adjustment methods such as the RPSFTM and IPCW mean that they are appropriate in different scenarios. In some scenarios, both methods may be prone to bias; "2-stage" methods should be considered, and intention-to-treat analyses may sometimes produce the least bias. The data requirements of adjustment methods also have important implications for clinical trialists.

A comparison of cost per case detected of screening strategies for Type 2 diabetes and impaired glucose regulation: modelling study.

BACKGROUND: To determine a cost per case detected for different screening strategies for both Type 2 diabetes alone and in combination with impaired glucose regulation. METHODS: Bayesian framework modelling study using data from the ADDITION-Leicester screening study in UK multi-ethnic primary care setting. There were 5794 people aged 40-75 years (77.4% white European; 22.6% south Asian) without previously known diabetes. We compared 212 screening strategies including blood tests, a computer practice data score and a risk score, as part of a multi-stage process that all used an oral glucose tolerance test as the diagnostic test. Simulation models were created using sensitivity estimates for the expected cost per case. RESULTS: The estimated costs per case identified for the 18 most sensitive strategies varied from £457 to £1639 (€526-1886, for £1=€1.15) for diabetes and £148-913 (€170-1050) for both diabetes and impaired glucose regulation. The lowest costing diabetes strategies ranged from £457 to £523 (€526-601) involving a two-stage screening strategy, a non-invasive risk stratifying tool followed by a blood test, producing sensitivities ranging from 67.1 to 82.4%. CONCLUSION: Screening a population using a non-invasive risk stratification tool followed by a screening blood test is the most cost-effective method of screening for diabetes and abnormal glucose tolerance.

When to wait for more evidence? Real options analysis in proton therapy.

PURPOSE: Trends suggest that cancer spending growth will accelerate. One method for controlling costs is to examine whether the benefits of new technologies are worth the extra costs. However, especially new and emerging technologies are often more costly, while limited clinical evidence of superiority is available. In that situation it is often unclear whether to adopt the new technology now, with the risk of investing in a suboptimal therapy, or to wait for more evidence, with the risk of withholding patients their optimal treatment. This trade-off is especially difficult when it is costly to reverse the decision to adopt a technology, as is the case for proton therapy. Real options analysis, a technique originating from financial economics, assists in making this trade-off. METHODS: We examined whether to adopt proton therapy, as compared to stereotactic body radiotherapy, in the treatment of inoperable stage I non-small cell lung cancer. Three options are available: adopt without further research; adopt and undertake a trial; or delay adoption and undertake a trial. The decision depends on the expected net gain of each option, calculated by subtracting its total costs from its expected benefits. RESULTS: In The Netherlands, adopt and trial was found to be the preferred option, with an optimal sample size of 200 patients. Increase of treatment costs abroad and costs of reversal altered the preferred option. CONCLUSION: We have shown that real options analysis provides a transparent method of weighing the costs and benefits of adopting and/or further researching new and expensive technologies.